This is mostly aimed at osu! mappers/storyboarders who need an easy way to access osu! beatmap folders through wine.

This is a quick tutorial on how you can edit some wine registry files to make wine open the native linux file explorer when clicking the Open folder menu on whatever application uses it, since the explorer.exe application doesn't integrate that well with the linux environment.

The main use-case for this workaround is the case of those who need to work with a high bandwidth of files being moved around and shared/edited/whatever it's needed to do with it. ^{eg. osu!mappers}

A 40-minute video holds maybe six minutes of signal. You either burn the 40 minutes, or you skip it and never find out. This skill reads the whole thing for you and hands back the structure: the hook, the beats, the timestamps that matter, and the script formula you can reuse.

See also the FAQ for Aspiring Quants.

• 3Red Partners
• A Priori Investments
• Acadian Asset Management
• Akuna Capital
• Albatross Labs
• AlphaGrep
• AlphaSimplex
• Alphataraxia Management

The Blood of Dawnwalker Trainer + Mod Manager 2026 for Windows with Human / Vampire Profiles, Day / Night Presets, Time Management Controls, Gameplay Quick Profiles, Inventory / Progression Utility, Mod Install, profiles, hotkeys, configs, and a clean desktop workflow.

Частично проверенный универсальный шаблон маршрутизации с русским руководством и английскими инструкциями агентов. Модельные маршруты подтверждены в CLI; техническое отключение рекурсии остаётся UNVERIFIED. Подготовлен 14 сентября 2026 года. Исходный codex-routing-gist.zip недоступен и не использовался: комплект собран заново по требованиям и официальной документации. Он не содержит пользовательской конфигурации или исходников проекта.

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.